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Early Warning Signals - Analytics for Learn (FSE Pilot)


Why do we need Early Warning Signals?

Some students encounter problems at the start of their studies but often this becomes visible when the exam (at the end of the course) is not passed. By signaling sub-optimal study behavior in the first weeks of a course study and providing timely interventions, behavior can be changed. Interventions can be diverse such as advice from a study advisor, extra guidance from a lecturer, or students studying more or differently. Analytics for Learn (A4L) can provide early warning signals. 


What is Analytics for Learn?

Analytics for Learn is a system from Blackboard that provides course analytics in a report format. Information about activity and grades in the electronic learning environment (Nestor/Blackboard) will be combined with information from the Student Information System (SIS/Progress.NET). 

To further introduce this topic with a specific example: within the first year of almost all courses at the Faculty of Science and Engineering (FSE), the project 'Early Warning Signals' has been running since mid 2019. This pilot at FSE aims to use study data to gain insight into the progress of students and their involvement in education even during the course of a course. The aim is to assess whether the use of study data can contribute to the quality of the education and supervision of students. If this is something that could be used in your courses and/or faculty, please read on for more information.


Where will the data come from?

The study data to be used is already in Nestor and/or Progress. A specific module within Nestor, Analytics for Learning, summarizes that data and presents it in different reports: either for students, teachers or student advisors.   


What can be seen in the reports and how are they used?

Students see their own study data for each course in which they are enrolled: they see their own results on intermediate tests and the amount of time they spent on Nestor within the course. Besides that, they see the group average on those same (intermediate) tests. With this they can compare their own results with the average of their fellow students in the course. Especially for first-year students, this gives them something to hold on to in comparison to their peers. This has a reassuring effect where things are going well and can be an impulse for more effort or contact with the teacher, study coach or student advisors where progress is not as expected. The earlier in the course students know the state of affairs the easier it is to take successful action. 

Teachers can see a clear summary of the learning results of their students in their own subject (interim) tests. This summary makes it easier for the teacher to see whether the intended (weekly) learning goals are being achieved. The quick reports give an early indication of whether a subject still requires extra attention in a class. In addition, the teacher can advise students to pay extra attention to subjects that are not (yet) at the required level, but must be mastered before the final exam.

Student advisors receive reports from their students within the faculty. Through smart selection, the study advisor can selectively invite groups of students for an individual interview to find out what the reason is for the visible study delay. The student advisor can then work with the student to discuss solutions that will maximize the student's chances of success over time. 


The Future

Virtually every institution in higher education is struggling with rising tuition costs. Many students experience some degree of study delay during their studies. However, studying/analyzing study data can provide insight into causes that would otherwise go unnoticed. Through better-targeted guidance impulses by student advisors or better and faster feedback to students, it may be possible to improve the throughput within the education system. This is good for all parties. Teachers need to review fewer resits, planners need fewer large halls, student advisors can work in a more targeted manner, and students graduate a little faster and gain a better understanding of their progress.


The use of study data and privacy

The use of study data is of course bound by rules. Before the start of the project, a Privacy Impact Assessment (PIA) was carried out. This showed that the way the already available data is summarized has no additional impact on the legal protection of student privacy.

For each course that uses EWS, each student within the course is shown a video on Nestor explaining the use of the data. In addition, a privacy statement document is available.

Finally, we ensure that the content of the reports does not in any way lead to automated decisions or messaging. Each student receives the same information and can judge for themselves whether and what action is needed based on their own situation.

Of course, despite all these precautions, there may still be concerns. Both on the side of the user/student, but also on the side of the organization. Within the project, we ensure that any future changes in working methods will require a review of whether and how student privacy is safeguarded and whether the use of data does not implicitly or explicitly have an undesirable impact on individuals or subgroups within the student population. 


Whom to Contact?

For more information please contact: J.T.Groenewoud@rug.nl 

Last modified: 6 January 01:18 PM
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